Software Solutions Architect

Nvidia

Confirmed live 2 days ago High trust

Remote

Quick summary

Work type: Remote
Location: NC
Salary: $152,000–$241,500 / yr
Posted: 14 days ago
Freshness: Confirmed live 2 days ago
Closes: Sep 25, 2026
Nearby: 99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $199k

This role $197k

$140k most similar roles pay here $262k

This role pays more than 52% of similar roles. Most pay $162,000–$235,750 — the shaded band above. At the midpoint, this role pays about $197k versus about $199k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 896 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 876 roles with salary data.

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View all roles at Nvidia

At a glance

TL;DR · Software Solutions Architect

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As a Software Solutions Architect, NVIS, you will join the NVIS team to develop the next generation of NVIS Central, an agentic software platform designed to automate and simplify delivery organization workflows. You will build and productionize LLM-powered agents, tool-calling workflows, backend services, APIs, data pipelines, and automation features. Your daily work involves translating operational needs into technical builds that reason across project data, knowledge bases, and logs while implementing retrieval-augmented generation, context management, and guardrails to ensure reliable AI systems. You will utilize Python, Docker, Kubernetes, Git, SQL, and NoSQL databases to build scalable solutions. The role focuses on the specific domain of building out large-scale AI Factory infrastructure for advanced accelerated computing environments, requiring expertise in high-performance computing clusters, multi-agent systems, and complex workflow automation within a fast-paced technical environment.

Skills

LLM RAG Python Agentic Workflows RESTful APIs Docker Kubernetes CI/CD SQL NoSQL Linux Distributed Systems Data Pipelines Git Observability HPC InfiniBand SLURM NVIDIA DGX SuperPOD

What you'll do

Compose, build, and productionize agentic AI solutions, tools, and applications for the NVIS delivery organization.
Develop LLM-based agents featuring tool-calling workflows, orchestration logic, backend services, and data pipelines.
Translate field, delivery, operations, and product requirements into technical builds and automated workflows.
Build agents capable of reasoning across project data, knowledge bases, operational systems, and logs.
Integrate LLMs and agentic tools with internal systems, reporting tools, and enterprise data sources.
Implement reliable AI systems using RAG, context management, evaluation frameworks, and safety guardrails.
Contribute to engineering standards including code quality, testing, CI/CD, observability, and production support.

What we're looking for

B.Sc. degree or equivalent experience in Computer Science, Computer Engineering, or a related technical field.
5+ years of hands-on software development experience building production applications, platforms, automation tools, or AI-based systems.
Strong programming experience with Python and modern backend development.
Hands-on experience with LLMs, agentic workflows, tool/function calling, RAG, and AI application development.
Experience crafting RESTful APIs, data services, workflow automation, and integrations with enterprise systems.
Experience building reliable software around non-deterministic AI systems including testing, evaluation, monitoring, and failure handling.
Experience with Docker, Kubernetes, CI/CD, Git, observability, and cloud-native development practices.
Background with SQL and NoSQL databases, data modeling, querying, indexing, and data integration.
Experience building agent platforms, copilots, multi-agent systems, or MCP-style integrations (preferred).
Deep understanding of LLM application patterns like context engineering, retrieval quality, and AI safety guardrails (preferred).
Experience with AI infrastructure, HPC clusters, NVIDIA DGX systems, SuperPOD, Spectrum-X, Ethernet, InfiniBand, or SLURM (preferred).
Strong Linux, networking, security, SRE, or distributed systems background (preferred).

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